Erik Fransén

Graded persistent activity in entorhinal cortex neurons.

Working memory represents the ability of the brain to hold externally or internally driven information for relatively short periods of time. Persistent neuronal activity is the elementary process underlying working memory but its cellular basis remains unknown. The most widely accepted hypothesis is that persistent activity is based on synaptic reverberations in recurrent circuits. The entorhinal cortex in the parahippocampal region is crucially involved in the acquisition, consolidation and retrieval of long-term memory traces for which working memory operations are essential. Here we show that individual neurons from layer V of the entorhinal cortex—which link the hippocampus to extensive cortical regions—respond to consecutive stimuli with graded changes in firing frequency that remain stable after each stimulus presentation. In addition, the sustained levels of firing frequency can be either increased or decreased in an input-specific manner. This firing behaviour displays robustness to distractors; it is linked to cholinergic muscarinic receptor activation, and relies on activity-dependent changes of a Ca2+-sensitive cationic current. Such an intrinsic neuronal ability to generate graded persistent activity constitutes an elementary mechanism for working memory.

Temporal Frequency of Subthreshold Oscillations Scales with Entorhinal Grid Cell Field Spacing

Grid cells in layer II of rat entorhinal cortex fire to spatial locations in a repeating hexagonal grid, with smaller spacing between grid fields for neurons inmore dorsal anatomical locations. Data from in vitro whole-cell patch recordings showed differences in frequency of subthreshold membrane potential oscillations in entorhinal neurons that correspond to different positions along the dorsal-to-ventral axis, supporting a model of physiological mechanisms for grid cell responses.

Mechanism of Graded Persistent Cellular Activity of Entorhinal Cortex Layer V Neurons

Working memory is an emergent property of neuronal networks, but its cellular basis remains elusive. Recent data show that principal neurons of the entorhinal cortex display persistent firing at graded firing rates that can be shifted up or down in response to brief excitatory or inhibitory stimuli. Here, we present a model of a potential mechanism for graded firing. Our multicompartmental model provides stable plateau firing generated by a nonspecific calcium-sensitive cationic (CAN) current. Sustained firing is insensitive to small variations in Ca2+ concentration in a neutral zone. However, both high and low Ca2+ levels alter firing rates. Specifically, increases in persistent firing rate are triggered only during high levels of calcium, while decreases in rate occur in the presence of low levels of calcium. The model is consistent with detailed experimental observations and provides a mechanism for maintenance of memory-related activity in individual neurons.

mGluR-dependent persistent firing in entorhinal cortex layer III neurons

Persistent firing is believed to be a crucial mechanism for memory function including working memory. Recent in vivo and in vitro findings suggest an involvement of metabotropic glutamate receptors (mGluRs) in persistent firing. Using whole‐cell patch‐recording techniques in a rat entorhinal cortex (EC) slice preparation, we tested whether EC layer III neurons display persistent firing due to mGluR activation, independently of cholinergic activation. Stimulation of the angular bundle drove persistent firing in 90% of the cells in the absence of a cholinergic agonist. The persistent firing was typically stable for > 4.5 min at which point persistent firing was terminated by the experimenter. The average frequency of the persistent firing was 2.1 Hz, ranging from 0.4 to 5.5 Hz. This persistent firing was observed even in the presence of atropine (2 μm), suggesting that the persistent firing can occur independent of cholinergic activation. Furthermore, ionotropic glutamate and GABAergic synaptic blockers (2 mm kynurenic acid, 100 μm picrotoxin and 1 μm CGP55845) did not block the persistent firing. On the other hand, blockers of group I mGluRs (100 μm LY367385 and 20 μm MPEP) completely blocked or suppressed the persistent firing. An agonist of group I mGluRs (20 μm DHPG) greatly enhanced the persistent firing induced by current injection. These results indicate that persistent firing can be driven through group I mGluRs in entorhinal layer III neurons, suggesting that glutamatergic synaptic input alone could enable postsynaptic neurons to hold input signals in the form of persistent firing.

A model of cortical associative memory based on a horizontal network of connected columns

An attractor network model of cortical associative memory functions has been constructed and simulated. By replacing the single cell as the functional unit by multiple cells in cortical columns connected by long-range fibers, the model is improved in terms of correspondence with cortical connectivity. The connectivity is improved, since the original dense and symmetric connectivity of a standard recurrent network becomes sparse and asymmetric at the cell-to-cell level. Our simulations show that this kind of network, with model neurons of the Hodgkin-Huxley type arranged in columns, can operate as an associative memory in much the same way as previous models having simpler connectivity. The network shows attractor-like behaviour and performs the standard assembly operations despite differences in the dynamics introduced by the more detailed cell model and network structure. Furthermore, the model has become sufficiently detailed to allow evaluation against electrophysiological and anatomical observations. For instance, cell activities comply with experimental findings and reaction times are within biological and psychological ranges. By introducing a scaling model we demonstrate that a network approaching experimentally reported neuron numbers and synaptic distributions also could work like the model studied here.

Computational Modeling of Entorhinal Cortex

Abstract: Computational modeling provides a means for linking the physiological and anatomical characteristics of entorhinal cortex at a cellular level to the functional role of this region in behavior. We have developed detailed simulations of entorhinal cortical neurons and networks, with an emphasis on the role of acetylcholine in entorhinal cortical function. Computational modeling suggests that when acetylcholine levels are high, this sets appropriate dynamics for the storage of stimuli during performance of delayed matching tasks. In particular, acetylcholine activates a calcium‐sensitive nonspecific cation current which provides an intrinsic cellular mechanism which could maintain neuronal activity across a delay period. Simulations demonstrate how this phenomena could underlie entorhinal cortex delay activity as described in previous unit recordings. 191,164 Acetylcholine also induces theta rhythm oscillations which may be appropriate for timing of afferent input to be encoded in hippocampus and for extraction of individual stored sequences from multiple stored sequences. Lower levels of acetylcholine may allow sharp wave dynamics which can reactivate associations encoded in hippocampus and drive the formation of additional traces in hippocampus and entorhinal cortex during consolidation.

Low spiking rates in a population of mutually exciting pyramidal cells

In a recurrent artificial neural network, the units active in an attractor state typically reach their maximum activity value while the others are quiescent. In contrast, recordings of cortical cell activity in vivo rarely reveal cells firing at their maximum rate. This discrepancy has been one of the main arguments against using attractor networks as models of cortical associative memory.In this study we show that low-rate sustained after-activity can be obtained in a simulated network of mutually exciting pyramidal cells. This is achieved by assuming that the synapses in the network are of a saturating type. When the application of a monoamine neuromodulator is simulated, after-activity with firing rates around 60 s−1 can be produced. The firing pattern of the network was found to be similar to that of the experimentally most comparable system, the disinhibited hippocampal slice. The results obtained are robust against simulated biological variation and background noise.

Modeling activity-dependent changes of axonal spike conduction in primary afferent C-nociceptors

Action potential initiation and conduction along peripheral axons is a dynamic process that displays pronounced activity dependence. In patients with neuropathic pain, differences in the modulation of axonal conduction velocity by activity suggest that this property may provide insight into some of the pathomechanisms. To date, direct recordings of axonal membrane potential have been hampered by the small diameter of the fibers. We have therefore adopted an alternative approach to examine the basis of activity-dependent changes in axonal conduction by constructing a comprehensive mathematical model of human cutaneous C-fibers. Our model reproduced axonal spike propagation at a velocity of 0.69 m/s commensurate with recordings from human C-nociceptors. Activity-dependent slowing (ADS) of axonal propagation velocity was adequately simulated by the model. Interestingly, the property most readily associated with ADS was an increase in the concentration of intra-axonal sodium. This affected the driving potential of sodium currents, thereby producing latency changes comparable to those observed for experimental ADS. The model also adequately reproduced post-action potential excitability changes (i.e., recovery cycles) observed in vivo. We performed a series of control experiments replicating blockade of particular ion channels as well as changing temperature and extracellular ion concentrations. In the absence of direct experimental approaches, the model allows specific hypotheses to be formulated regarding the mechanisms underlying activity-dependent changes in C-fiber conduction. Because ADS might functionally act as a negative feedback to limit trains of nociceptor activity, we envisage that identifying its mechanisms may also direct efforts aimed at alleviating neuronal hyperexcitability in pain patients.

Cell Assembly Dynamics in Detailed and Abstract Attractor Models of Cortical Associative Memory

During the last few decades we have seen a convergence among ideas and hypotheses regarding functional principles underlying human memory. Hebb’s now more than fifty years old conjecture concerning synaptic plasticity and cell assemblies, formalized mathematically as attractor neural networks, has remained among the most viable and productive theoretical frameworks. It suggests plausible explanations for Gestalt aspects of active memory like perceptual completion, reconstruction and rivalry. We review the biological plausibility of these theories and discuss some critical issues concerning their associative memory functionality in the light of simulation studies of models with palimpsest memory properties. The focus is on memory properties and dynamics of networks modularized in terms of cortical minicolumns and hypercolumns. Biophysical compartmental models demonstrate attractor dynamics that support cell assembly operations with fast convergence and low firing rates. Using a scaling model we obtain reasonable relative connection densities and amplitudes. An abstract attractor network model reproduces systems level psychological phenomena seen in human memory experiments as the Sternberg and von Restorff effects. We conclude that there is today considerable substance in Hebb’s theory of cell assemblies and its attractor network formulations, and that they have contributed to increasing our understanding of cortical associative memory function. The criticism raised with regard to biological and psychological plausibility as well as low storage capacity, slow retrieval etc has largely been disproved. Rather, this paradigm has gained further support from new experimental data as well as computational modeling.

Dampening of hyperexcitability in CA1 pyramidal neurons by polyunsaturated fatty acids acting on voltage-gated ion channels.

A ketogenic diet is an alternative treatment of epilepsy in infants. The diet, rich in fat and low in carbohydrates, elevates the level of polyunsaturated fatty acids (PUFAs) in plasma. These substances have therefore been suggested to contribute to the anticonvulsive effect of the diet. PUFAs modulate the properties of a range of ion channels, including K and Na channels, and it has been hypothesized that these changes may be part of a mechanistic explanation of the ketogenic diet. Using computational modelling, we here study how experimentally observed PUFA-induced changes of ion channel activity affect neuronal excitability in CA1, in particular responses to synaptic input of high synchronicity. The PUFA effects were studied in two pathological models of cellular hyperexcitability associated with epileptogenesis. We found that experimentally derived PUFA modulation of the A-type K (KA) channel, but not the delayed-rectifier K channel, restored healthy excitability by selectively reducing the response to inputs of high synchronicity. We also found that PUFA modulation of the transient Na channel was effective in this respect if the channels steady-state inactivation was selectively affected. Furthermore, PUFA-induced hyperpolarization of the resting membrane potential was an effective approach to prevent hyperexcitability. When the combined effect of PUFA on the KA channel, the Na channel, and the resting membrane potential, was simulated, a lower concentration of PUFA was needed to restore healthy excitability. We therefore propose that one explanation of the beneficial effect of PUFAs lies in its simultaneous action on a range of ion-channel targets. Furthermore, this work suggests that a pharmacological cocktail acting on the voltage dependence of the Na-channel inactivation, the voltage dependences of KA channels, and the resting potential can be an effective treatment of epilepsy.